70 percent of the 2,341 respondents across 30 business sectors picked AR as disrupting their industry most out of a selection of seven emerging technologies: AI, cybersecurity, cloud computing, IoT, blockchain, and 5G.

Filipe Oliveira, Senior Analyst at GlobalData, commented: “This change in how people see AR will likely be long term, and not just a temporary blip. It is clear that people are warming towards the technology, even if they don’t believe that it will make a big difference tomorrow.” 

AI wins some ground back when it comes to confidence in the technology. 57 percent of the respondents believe that AI will live up to all of its promises compared to just 26 percent for AR.

Along those same lines, 31 percent believe “The technology is hyped, but I can see a use for it” for AI, while a huge 50 percent report the same for AR.

Apple’s decision to add a LiDAR sensor to its latest mobile devices was seen as an important step towards the mass adoption of AR. Excitement is also growing around so-called “metaverses” that converge virtually-enhanced physical reality with physically-persistent shared virtual spaces.

SenseTime, one of China’s leading AI companies, announced earlier this week that it had partnered with BilibiliWorld to create a metaverse. The experience leverages SenseTime’s AI and mixed reality technologies to enable players to enjoy role-playing games that seamlessly blend reality with virtuality.

Facebook CEO Mark Zuckerberg recently said the company “will effectively transition from people seeing us as primarily being a social media company to being a metaverse company”. As the owner of Oculus, Zuckerberg’s plans for the future of Facebook will likely make people think of a large virtual space similar to that depicted in Ernest Cline’s Ready Player One novel and the 2018 film adaptation.

Some people have expressed concern about a large centralised company such as Facebook having control over such a potentially ubiquitous world and the content they consume. Many believe that an open-source decentralised version is vital:

Zuckerberg, for his part, claims that no one company will run the metaverse and it will be an “embodied internet” that is operated by many different players.

Decentraland is an early example of what a truly decentralised virtual space could look like. The platform makes use of a DAO (Decentralised Autonomous Organisation) to make policy decisions such as what content is allowed in addition to taking advantage of the NFT (Non-Fungible Token) trend to offer exclusive in-world items.

AR and AI are both important emerging technologies that can often go hand-in-hand, but it’s clear that the latter is losing its perspective among professionals as having the biggest impact on their industries over the coming years.

(Photo by My name is Yanick on Unsplash)

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Most current AR applications are somewhat gimmicky, with barely a handful that have achieved any form of mass adoption. Apple’s decision to introduce LiDAR in its recent devices has given AR a boost but it’s clear that more needs to be done to make applications more useful.

A newly filed patent suggests that Apple is exploring how machine learning can be used to automatically (or “automagically,” the company would probably say) detect objects in AR.

The first proposed use of the technology would be for Apple’s own Measure app.

Measure’s previously dubious accuracy improved greatly after Apple introduced LiDAR but most people probably just grabbed an actual tape measure unless they were truly stuck without one available.

The patent suggests machine learning could be used for object recognition in Measure to help users simply point their devices at an object and have its measurements automatically presented in AR.

Specifically, Apple’s patent suggests displaying a “measurement of the object determined using one of a plurality of class-specific neural networks selected based on the classifying of the object.”

This simplicity benefit over a traditional tape measure would likely drive greater adoption.

Machine learning is already used for a number of object recognition and labelling tasks within Apple’s ecosystem. Image editor Pixelmator Pro, for example, uses it to automatically label layers.

Apple’s implementation suggests an object is measured “by first generating a 3D bounding box for the object based on the depth data”. This boundary box is then refined “using various neural networks and refining algorithms described herein.”

Not all objects are measured the same so Apple suggests that a neural network could also step in here to determine what could be useful for the user. For example, “a seat height for chairs, a display diameter for TVs, a table diameter for round tables, a table length for rectangular tables, and the like.”

To accomplish what Apple envisions here, a lot of models will need to be trained for all objects. However, there are many of the more everyday items that could be supported early on—with more added over time.

“One model may be trained and used to determine measurements for chair type objects (e.g., determining a seat height, arm length, etc.),” Apple wrote, “and another model may be trained and used to determine measurements for TV type objects (e.g., determining a diagonal screen size, greatest TV depth, etc.)”

Five inventors are credited with the patent: Amit Jain, Aditya Sankar; Qi Shan, Alexandre Da Veiga, and Shreyas V Joshi.

Apple’s patent is another example of how machine learning can be combined with other technologies to add real utility and ultimately improve lives. There’s no telling when, or even if, Apple will release an updated Measure app based on this patent—but it seems more plausible in the not-so-distant future than many of the company’s patents.

(Image Credit: Apple)

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Dr Alex Young is a trained orthopaedic and trauma surgeon who founded Virti and set out to use emerging technologies to provide immersive training for both new healthcare professionals and experienced ones looking to hone their skills.

COVID-19 has put most in-person training on hold to minimise transmission risks. Hospitals and universities across the UK and US are now using the virtual patient as a replacement—including our fantastic local medics and surgeons at the Bristol NHS Foundation Trust.

The virtual patient uses Natural Language Processing (NLP) and ‘narrative branching’ to allow medics to roleplay lifelike clinical scenarios. Medics and trainees can interact with the virtual patient using their tablet, desktop, or even VR/AR headsets for a more immersive experience.

Dr Alex Young comments:

“We’ve been working with healthcare organisations for several years, but the pandemic has created really specific challenges that technology is helping to solve. It’s no longer safe or practicable to have 30 medics in a room with an actor, honing their clinical soft-skills. With our virtual patient technology, we’ve created an extremely realistic and repeatable experience that can provide feedback in real time. This means clinicians and students can continue to learn valuable skills.

Right now, communication with patients can be very difficult. There’s a lot of PPE involved and patients are often on their own. Having healthcare staff who are skilled in handling these situations can therefore make a huge difference to that patient’s experience.”

Some of the supported scenarios include: breaking bad news, comforting a patient in distress, and communicating effectively whilst their faces are obscured by PPE. Virti’s technology was also used at the peak of the pandemic to train NHS staff in key skills required on the front line, such as how to safely use PPE, how to navigate an unfamiliar intensive care ward, how to engage with patients and their families, and how to use a ventilator.

Tom Woollard, West Suffolk Hospital Clinical Skills and Simulation Tutor, who used the Virti platform at the peak of the COVID pandemic, comments:

“We’ve been using Virti’s technology in our intensive care unit to help train staff who have been drafted in to deal with COVID-19 demand.

The videos which we have created and uploaded are being accessed on the Virti platform by nursing staff, physiotherapists and Operational Department Practitioners (ODPs) to orient them in the new environment and reduce their anxiety.

The tech has helped us to reach a large audience and deliver formerly labour-intensive training and teaching which is now impossible with social distancing.

In the future, West Suffolk will consider applying Virti tech to other areas of hospital practice.”

The use of speech recognition, NLP, and ‘narrative branching’ provides a realistic simulation of how a patient would likely respond—providing lifelike responses in speech, body language, and mannerisms.

The AI delivers real-time feedback to the user so they can learn and improve. With upwards of 70 percent of complaints against health professionals and care providers attributable to poor communication, the virtual patient could help to deliver better care while reducing time spent handling complaints.

Virti’s groundbreaking technology has – quite rightly – been named one of TIME’s best inventions of 2020.

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Companies around the world are having to look at new ways of safely continuing business as we adapt to the “new normal” of life with the coronavirus.

Amazon has used its AI expertise to create what it calls the Distance Assistant. Using a time-of-flight sensor, often found in modern smartphones, the AI measures the distance between employees.

The AI is used to differentiate people from their background and what it sees is displayed on a 50-inch screen for workers to quickly see whether they’re adhering to keeping a safe distance.

Augmented reality is used to overlay either a green or red circle underneath each employee. As you can probably guess – a green circle means that the employee is a safe distance from others, while a red circle indicates that person needs to give others some personal space.

The whole solution is run locally and does not require access to the cloud to function. Amazon says it’s only deployed Distance Assistant in a handful of facilities so far but plans to roll out “hundreds” more “over the next few weeks.”

While the solution appears rather draconian, it’s a clever – and arguably necessary – way of helping to keep people safe until a vaccine for the virus is hopefully found. However, it will strengthen concerns that the coronavirus will be used to normalise increased surveillance and erode privacy.

Amazon claims it will be making Distance Assistant open-source to help other companies adapt to the coronavirus pandemic and keep their employees safe.

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Xavier is designed to power the NVIDIA DRIVE software stack which has been expanded to a trio of AI platforms covering every aspect of the experience inside next-generation automobiles.

DRIVE AV — This part of the DRIVE platform was also available in the previous generation and uses neural networks to perform the calculations required for self-driving cars.

DRIVE IX — The first of the two new inclusions is a software development kit which enables AI assistants that can harness data from sensors inside and outside the vehicle.

DRIVE AR — While there have been many advancements around self-driving technologies and AI assistants for cars, augmented reality is still relatively unexplored. With DRIVE AR, Nvidia intends to enable new graphical experiences which can deliver things such as information about points of interest along the route.

The processor has been in development for over four years and represents the work of over 2,000 engineers. Xavier features more than nine billion transistors and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created.

Autonomous vehicles require data to be captured and processed at very fast rates. To help achieve this, Xavier is built around a custom 8-core CPU. The processor also includes a new 512-core Volta GPU, deep learning accelerator, new computer vision accelerators, and 8K HDR video processors.

These features come together to calculate 30 trillion operations per second while consuming just 30 watts. Nvidia claims this generation of its DRIVE platform is 15 times more energy efficient than its previous.

Nvidia Pegasus is the company’s supercomputer for self-driving cars. It uses two Xavier SoCs in tandem with two next-generation GPUs to deliver 320 trillion operations per second of performance.

Customers are due to receive their first samples of Pegasus in mid-2018.

What are your thoughts on Nvidia’s new Xavier SoC? Let us know in the comments.

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